Compute MinHashes in a generator. This method avoids unnecessary overhead when initializing many minhashes by reusing the initialized

Parameters ---------- score_distributions : List[np.ndarray] The samples of all existing scores for each of the L

Get the DataFrames of each table in a folder Args: dataFolder: filepath to the folder with all tables Return: dataDfs (di

Get the DataFrames of each table in a folder Args: dataFolder: filepath to the folder with all tables Return: dataDfs (di

Retrieve the embedding dimensions of the underlying model. Returns ------- int The dimensions of each emb

Args: file_path (): Returns: dict {querytable:union tables and colums}

Get all the keys. Note: selecting all keys in Cassandra via "SELECT DISTINCT key FROM table" is bound to tim

Extract the most representative tokens of each value and return the token set. Here, the most representative tokens are the ones with

Extract the most representative tokens of each value and return the token set. Here, the most representative tokens are the ones with

Retrieve the embedding of the given word. If the word is out of vocabulary a zero vector is returned. Parameters ----

Retrieve the embedding of the given word. If the word is out of vocabulary a zero vector is returned. Parameters ----

Groups column-based results by table. For a given query column, at most one candidate column is considered for each candidate table.

Determines whether the key is in the storage or not